Addressing GPU On-Chip Shared Memory Bank Conflicts Using Elastic Pipeline

Abstract

One of the major problems with the GPU on-chip shared memory is bank conflicts. We analyze that the throughput of the GPU processor core is often constrained neither by the shared memory bandwidth, nor by the shared memory latency (as long as it stays constant), but is rather due to the varied latencies caused by memory bank conflicts. This results in… (More)
DOI: 10.1007/s10766-012-0201-1

Topics

15 Figures and Tables

Statistics

0102030201520162017
Citations per Year

Citation Velocity: 6

Averaging 6 citations per year over the last 3 years.

Learn more about how we calculate this metric in our FAQ.

Cite this paper

@article{Gou2012AddressingGO, title={Addressing GPU On-Chip Shared Memory Bank Conflicts Using Elastic Pipeline}, author={Chunyang Gou and Georgi Gaydadjiev}, journal={International Journal of Parallel Programming}, year={2012}, volume={41}, pages={400-429} }